Mirror detection aims to identify the mirror regions in the given input image. Existing works mainly focus on integrating the semantic features and structural features to mine the similarity and discontinuity between mirror and non-mirror regions, or introducing depth information to help analyze the existence of mirrors. In this work, we observe that a real object typically forms a loose symmetry relationship with its corresponding reflection in the mirror, which is beneficial in distinguishing mirrors from real objects. Based on this observation, we propose a dual-path Symmetry-Aware Transformer-based mirror detection Network (SATNet), which includes two novel modules: Symmetry-Aware Attention Module (SAAM) and Contrast and Fusion Decoder Module (CFDM). Specifically, we first introduce the transformer backbone to model global information aggregation in images, extracting multi-scale features in two paths. We then feed the high-level dual-path features to SAAMs to capture the symmetry relations. Finally, we fuse the dual-path features and refine our prediction maps progressively with CFDMs to obtain the final mirror mask. Experimental results show that SATNet outperforms both RGB and RGB-D mirror detection methods on all available mirror detection datasets.

翻译：现有工作主要侧重于将语义特征和结构特征与镜像相似性和不连续性结合起来,或引入深度信息以帮助分析镜像的存在。在这项工作中,我们观察到,一个真实物体通常形成松散的对称关系,其反射在镜子中也相应反映,这有利于区分镜像与真实物体。根据这一观察,我们提议建立一个双向对称-软件变换镜探测网(SATNet),其中包括两个新型模块:对称-软件注意模块(SAAM)以及对比和融合解密模块(CFDDM)。具体地说,我们首先引入变异主干线,以模拟图像中的全球信息集合,在两条路径中提取多尺度特征。然后我们将高层次的双向特征提供给SAAMs,以捕捉对称关系。最后,我们将双向特征与我们的预测地图与CFDMs相融合,以获得最后镜镜镜镜掩码。我们先行的实验性卫星探测结果显示,在RGB探测方法上,所有可得到的RGB探测器。